Data Quality stories
Despite widespread adoption, nearly a third of UK finance chiefs cannot quantify AI's impact on cash and liquidity management, a survey found.
Nearly a third of UK finance leaders cannot quantify AI's benefits, highlighting a gap between enthusiasm and proof as liquidity teams face rising pressure.
The hire comes as the company steps up product investment after reporting 46% revenue growth for the year to June 2026.
Only 26% of IT decision-makers say they can resolve conflicts across AI agents, exposing a readiness gap as firms expand deployment.
Many ANZ firms lack the data governance and integration needed to prove AI is lifting productivity, costs or customer experience.
Synthetic fraud is slipping past standard checks, prompting banks to use real-time address matching to tie identities to physical locations.
Many employers cannot link above-market pay to performance, with 58% assessing fewer than half of significant premiums against business outcomes.
Most data programmes fail on culture, not code, with 93.2% of senior executives citing change management as the main barrier to adoption.
Poor property data can distort valuations, tenant checks and investment decisions, so real estate teams need strict verification before systems use it.
Inaccurate records are slowing service activations and could distort telecom valuations, with 78% of operators saying data quality affects mergers and acquisitions.
Most organisations are still losing time on manual data prep and siloed records, with 86% reporting AI disruption in the past year.
The acquisition gives enterprises extra controls to stop bad data from skewing AI outputs and automated actions at scale.
Despite weak data foundations, 65 per cent of local organisations are already running AI projects, while many cannot measure success effectively.
Most Australian executives still lack the data controls needed to trust AI in reporting, even as 95 per cent expect a return within a year.
Most transport and logistics operators plan bigger AI budgets next year, as 90% say returns have met or exceeded expectations despite readiness gaps.
Businesses were urged to fix data, processes and governance first, as AI in ERP was shown to depend on stronger operational basics.
Cleaner customer records can cut undeliverables, reduce wasted marketing spend and help retailers deliver smoother holiday orders and campaigns.
That shortfall is already hitting projects, with 37% of firms pausing, scaling back or abandoning AI initiatives because of poor data quality or availability.
Only 28% of firms in Komprise's survey have measured AI returns, despite 68% saying their organisations are already maturing or mature in AI.
The tie-up aims to fix a long-standing data gap in IT operations, giving service teams richer context to speed incident handling and cut risk.